Nowadays, sequential recommender systems have been widely deployed on various application platforms for recommending potentially interested items to users. Typically, such a recommendation task is formulated as a sequence prediction problem [1]}, [2]}, [3]}, [4]}, inferring the next item(s) that a user is likely to interact with based on her/his historical interaction sequences. Although similar task formulation has been taken for different sequential recommenders, it is difficult to reuse an existing well-trained recommender for servicing new recommendation scenarios [5]}, [6]}. For example, when a new domain emerges with specific interaction characteristics, it may need to train a recommender from scratch, which is time-consuming and also easy to suffer from the cold-start issue. Thus, it is desired to develop transferable sequential recommenders [7]}, [6]}, [9]}, which can quickly adapt to new domains or scenarios.
